Decision tree induction from numeric data stream

Satoru Nishimura, Masahiro Terabe, Kazuo Hashimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Hoeffding Tree Algorithm is known as a method to induce decision trees from a data stream. Treatment of numeric attribute on Hoeffding Tree Algorithm has been discussed for stationary input. It has not yet investigated, however, for non-stationary input where the effect of concept drift is apparent. This paper identifies three major approaches to handle numeric values, Exhaustive Method, Gaussian Approximation, and Discretizaion Method, and through experiment shows the best suited modeling of numeric attributes for Hoeffding Tree Algorithm. This paper also experimentaly compares the performance of two known methods for concept drift detection, Hoeffding Bound Based Method and Accuracy Based Method.

Original languageEnglish
Title of host publicationAI 2008
Subtitle of host publicationAdvances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings
Pages311-317
Number of pages7
DOIs
Publication statusPublished - 2008 Dec 1
Externally publishedYes
Event21st Australasian Joint Conference on Artificial Intelligence, AI 2008 - Auckland, New Zealand
Duration: 2008 Dec 12008 Dec 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5360 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st Australasian Joint Conference on Artificial Intelligence, AI 2008
CountryNew Zealand
CityAuckland
Period08/12/108/12/5

Keywords

  • Concept drift
  • Hoeffding tree
  • Numeric data stream

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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